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The problem of economic dispatch is one of the significant topics in modern power system control, monitoring, and operation studies due to economic and environmental issues. The multi-area economic dispatch problem (MAED) is the extended version of the economic dispatch problem in modern, and interconnected power systems, especially in competitive environments, which leads to the improvement of power networks economically and technically. The main goal of the MAED problem is to find the optimal amounts of generation and power interchange between adjacent areas by minimizing the generation, and transmission costs, satisfying different operational, and physical constraints governing the problem. This study endeavors to present a comprehensive classification of different techniques, and methods applied to the multi-area economic dispatch problem while reviewing the most prominent studies in this field. Also, it covers comprehensive formulations of the problem and some important issues in the field of probabilistic MAED. Furthermore, some concepts, such as used test systems, and hardware specification are addressed. Finally, suggestions and future directions are highlighted. [Display omitted] • Presenting the formulation of MASED, MADED, RCMAED, RCMAEED, MAEED, and MANCEED. • Classify the methods based on problem-solving, and mechanism handling techniques. • Describing the mathematical-based optimization, and meta-heuristic methods. • Addressing the single-objective, and multi-objective proposed algorithms. • Detailing the probabilistic MAED techniques, and some data of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, correlation between uncertainties related to loads is addressed in transmission network expansion planning (TNEP). The TNEP problem is formulated in a multi-objective form with objective functions of minimum investment cost, minimum congestion cost, and minimum risk cost. Load correlation is modeled using unscented transformation method and compared to a simulation method. Initially, the impact of load correlation on congestion cost and risk cost is shown. Then, using the Pareto solutions of this multi-objective TNEP problem, impact of load correlation on the TNEP solutions is illustrated. The Pareto solutions are obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA II) that is capable in handling non-convex and non-linear problems with mixed integer structures. Test results are provided for the IEEE 24-bus Reliability Test System (RTS) and a representation of the actual Iranian 400 kV transmission network. [ABSTRACT FROM AUTHOR]

Abstract Environmental concerns have caused application of renewable energy sources as a clean appropriated alternative for conventional thermal generators in power system planning. These uncertain and variable sources have challenged the power system scheduling to ensure that it still remains reliable and economical. Also, plug-in electric vehicles may potentially alter load demand in uncontrolled charging mode and impose new challenges to the unit commitment problem. This paper demonstrates the fast heuristic method based on priority list to solve the stochastic unit commitment problem and applied it to a basic 10-unit case study system accompaniment with an electric vehicle parking lot, a wind farm and a solar farm over a 24-h time horizon. As it is reported, scenario generation with Monte Carlo simulation can relatively compensate for the intermittent behavior of renewable energy sources and result in more economical and robust planning. Moreover, the integration of plug-in electric vehicles and their controlled charging/discharging is considered. Thermal units are scheduled by priority list method with their relevant constraints such as minimum up/down time, spinning reserve, load demand and capacity limitation. The simulations results verify that the penetration of the mentioned renewable energy sources can improve the operational cost and computation time, effectively. Highlights • Presenting a fast heuristic priority list-based method to solve the stochastic unit commitment. • Considering the electric vehicles, wind and solar farms in the problem. • Applying the scenario generation from appropriate distribution with Monte Carlo simulation. • Reporting the case study results regarding to a basic 10-unit case study system. [ABSTRACT FROM AUTHOR]

Due to the environmental concerns and the high penetration of wind power (WP) and electric vehicles (EVs), reducing the generation costs and emissions, coordinating EV charging in the distribution networks, and addressing the WP uncertainty, are among the most challenging issues in power system control and operation. Hence, the multi-objective dynamic economic emission dispatch problem (MDEEDP) integrated with EVs and wind farms has become one of the hottest topics in this research area. In this paper, the MDEEDP in the presence of EVs, the random behavior of EV drivers, and WP uncertainty is solved using a new multi-objective evolutionary algorithm called the enhanced multi-objective exchange market algorithm. In the proposed algorithm, the novel point-to-point distance technique is introduced to find the Pareto front solutions set (PFSS) with a uniform and diverse distribution. Also, smart strategy management for charging and discharging EVs is proposed to smooth the load curve. The effectiveness of the proposed method and performance of the proposed smart strategy are investigated on two single-objective and multi-objective test systems, including EVs and wind farms, considering uncertainty and system constraints such as ramp rate limits. The simulation results show that the PFSS of the MDEEDP is well extracted by the proposed method while maintaining its uniformity and diversity. The presence of EVs and the application of the proposed smart strategy lead to a reduction of 2,926,497 ($) in the operation costs for test system 1, and, 38,694,698.28 ($) and 7,832,876.64 (kg), respectively in the operation costs and emissions for test system 2, assuming the same load curve throughout the year. In addition, the load factor in test systems 1, and 2 is improved by 11 and 19 percent, respectively. [ABSTRACT FROM AUTHOR]

Energy hubs can provide high flexibility in the operation of the integrated systems. This paper presents Time Varying Acceleration Coefficients Particle Swarm Optimization (TVAC-PSO) algorithm to optimize the Multiple Energy Carriers Economic Dispatch (MECED) problem for hybrid electrical and natural gas networks. The simulation results on two case studies are reported. The first one verifies an introduced gas loss formula and another one is devoted to a modified version of IEEE 14-bus test system. The results are compared with PSO, Genetic Algorithm (GA), and Differential Evolution (DE) technique. They show that a hybrid system can operate at a lower cost than independently-operated systems; CHP units can supply more electrical demand than the electrical network while their contribution in supplying the heat demand may be smaller than that of gas furnaces. In a hybrid system, energy efficiency can be reached, producing electrical power and heat locally; CHP units have a great role in reduction of operational cost; In the coupled mode, electrical power losses are decreased, while the gas losses are increased; The simple proposed gas loss formula can provide acceptable results; Cost reductions due to employing CHPs and applying the proposed optimization technique are greater than the other methods. [ABSTRACT FROM AUTHOR]

This research work proposes the hybrid meta-heuristic algorithm to solve the multi-area economic dispatch problem to operate the power systems economically. The proposed hybrid algorithm combines the exchange market and grasshopper optimization algorithms. The grasshopper optimization algorithm sometimes suffers from premature convergence. To overcome this drawback, a hybrid algorithm for the better balance between exploration and exploitation and thus finding optimal solutions with high quality and robustness, is proposed in this paper. The main target of this paper is to investigate the application of the hybrid algorithm for optimizing the multi-area economic dispatch problem, satisfying different constraints of the valve point loading effect, multiple fuels, prohibited operation zones, ramp-rate limit, the tie-line constraints, and transmission loses. In this regard, after validation of the proposed hybrid algorithm by applying it on some benchmark functions of diverse nature, the effectiveness and robustness of the proposed algorithm are evaluated in three case studies of the multi-area economic dispatch problem. The comparative results confirmed the effectiveness of the proposed method in terms of robustness and quality solutions. Also, the applied method reduces the fuel cost in the range of 0.0229%–1.1622%. • Introducing a new meta-heuristic algorithm based on the combination of EMA and GOA. • Using the EMA-GOA to optimize some well-known benchmark functions. • Applying the algorithm for the first time to different kinds of multi-area economic dispatch problems. • Investigating the proposed method performance on different types of the mentioned problems. [ABSTRACT FROM AUTHOR]

The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm, namely hybrid Time Varying Acceleration Coefficients-Gravitational Search Algorithm-Particle Swarm Optimization (hybrid TVAC-GSA-PSO), to solve the large-scale, highly nonlinear, non-convex, non-smooth, non-differential, non-continuous, and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The suggested algorithm is based on the Newtonian laws of gravitation and motion as well as swarm behaviors. The proposed technique combines the best performances of three optimization methods (i.e. GSA, TVAC-GSA, and TVAC-PSO) in terms of their specific strategies in particle movements through a self-adoptive learning strategy. The effectiveness and robustness of the suggested algorithm are tested on a set of five benchmark functions and two large-scale CHPED problems (considering valve-point loading effect and prohibited zones of conventional power units, transmission losses, as well as special characteristic of CHP units). The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function. [ABSTRACT FROM AUTHOR]

Power quality and power disturbances are the most important increasing factors throughout electrical networks. Ferroresonance as one of these disturbances can cause quality and security problems. This paper analyzes the stochastic behavior of ferroresonance as a nonlinear resonance phenomenon of the erratic nature and difficult prediction. Furthermore, the impact of different parameters on the initiation of ferroresonance is investigated. It is illustrated that a small change in the initial conditions leads to a large difference in the long-term behavior of the system, and this makes the future of the system unpredictable. Based on the stochastic behavior of ferroresonance some papers took different parameters into account and attempted to identify the basins of attraction for different ferroresonance regions. Here, according to real condition it is illustrated that these basins are highly depending on the initial conditions and there are no transparent boundaries for them. Also, by changing different parameters and initial conditions, the probability degree of ferroresonance initiation is calculated and depicted. Moreover, the impact of source angle on the ferroresonance initiation is investigated. [ABSTRACT FROM AUTHOR]

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient Gravitational Search Algorithm (TVAC-GSA), to solve both single- and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value. [ABSTRACT FROM AUTHOR]

• Improving the voltage in a wind farm by a self-corrective static VAR compensator. • Considering the steady-state and transient modes under harsh faults in analysis. • Enhancing the system performance by employing an optimized Battery Energy Storage. • Reporting the results in different cases, with or without the mentioned equipment. Maintaining synchronism and voltage stability, especially in the presence of wind farms, has a crucial role in confirming the reliability requirements of the power grid, as the natural fluctuations of wind speed result in intermittency and uncontrollability of power generation. To this end, an optimized Battery Energy Storage System (BESS) was suggested to make an equilibrium between the generation and consumption in wind farms under harsh conditions. Additionally, the instability of wind farms is very likely after transmission line faults occur. To overcome this problem, in this paper, the impact of a constant capacitor bank, connected in parallel with the wind generators, is investigated. Also, a novel self-corrective Static Volt-ampere reactive Compensator (SVC) is proposed to overcome the variations of voltage and damp the reactive power oscillations after occurring the faults. The proposed SVC compares the network voltage profile as the reference voltage with the voltage deviations in a control block diagram. For this purpose, three similar wind farms of Permanent Magnet Synchronous Generator (PMSG) type are considered in a wind park. The simulation case studies are performed in Matlab/Simulink under different conditions of transient and steady-state voltage stabilities in the presence of proposed BESS including, only capacitor bank (without self-corrective SVC), and with self-corrective SVC. The results authenticate the ability of the proposed SVC strategy to correct the voltage deviations, and voltage stability improvement of the under study system. [ABSTRACT FROM AUTHOR]

DED (Dynamic economic dispatch) problem schedules generation units during the whole dispatch period in order to minimize the fuel costs. On the other hand DRPs (Demand Response Programs) focus on increasing customers' benefit and improving network reliability. If these two problems are optimally integrated considering their interactions at each side, they will be implemented more effectively. One of the main concerns in the TOU (Time of Use) DRP is the optimal pricing during different periods. In this paper, TOU which focuses on the demand side has been intelligently integrated with the DED problem which focuses on the supply side. In the combined problem namely DEDTOU, a new procedure for the optimal pricing will be presented so that the fuel costs in the DED problem are minimized and the optimal prices during different periods i.e. valley, off-peak, and peak periods in TOU are determined simultaneously. By the way, not only the network reliability and customers' benefit are increased but also fuel costs are decreased and generation units are optimally scheduled. Actually, DEDTOU is a win-win game both for the demand and supply sides. DEDTOU is applied on a ten units test system and results indicate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The unit commitment problem is one of the most significant and basic issues in the monitor, control, and operation of modern power systems, which has always been a subject of great concern to researchers and operators as the most extensive human-made system. Before restructuring, one of the main objectives of unit commitment problem was the minimization of the total operation cost of power plants subject to various constraints, including unit and network ones. As the privatization and restructuring process became more serious, the primary purpose of the unit commitment problem has been changed to maximizing the total profit, which led to the emergence of a new concept known as profit-based unit commitment problem. Accordingly, the maximization of the profit for generation companies, all over the studied period, is a top-priority direction. This paper presents a comprehensive overview of the profit-based unit commitment problem in restructured power systems by investigating the most important studies on this topic and providing a complete classification. It also outlines the challenges facing researchers in this field, offers new insights, and suggests future directions. • Describing a comprehensive formulation for profit-based unit commitment problem. • Presenting a complete and comprehensive overview of profit-based unit commitment problem. • Reporting the used case study systems, simulation tools, hardware, and run-time. • Briefly reviewing the fitness functions, parameter setting, convergence criterion, and the obtained profit values. • Addressing the main challenging issues regarding the mentioned problem. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions. • Presenting a comprehensive model for long-term planning of various combined heat and power units. • Investigating the uncertainty of electric loads, and market price using information gap decision theory. • Modeling the long-term security of the power grid from the voltage stability viewpoint. • Introducing a risk-averse multi-objective planning model. • Considering the environmental objectives in the combined heat and power investment. [ABSTRACT FROM AUTHOR]

• Applying the GA-PLB strategy to limit the vast search space of the UCP by focusing on only some feasible solutions. • Initial checking conditions of MDT, MUT, and SR which reduce the computing time effectively. • Arranging the unit combinations and output power of thermal power plants, stored (injected) power in (by) ESSs applying GA. • Applying TOAT to overcome the hardness of uncertainty modeling in UUCP. The main purpose of unit commitment problem (UCP) is to find the optimal operation cost (OOC) by considering power system constraints. In this paper, an uncertain UCP (UUCP) is proposed in the presence of energy storage systems (ESSs) via applying genetic algorithm-priority list-based (GA-PLB) strategy and considering operational constraints such as power balance, minimum down time (MDT), minimum up time (MUT), and spinning reserve (SR). To find the robust optimal scheduling due to modeling the uncertainty of renewable energy sources (RESs), the Taguchi orthogonal arrays technique (TOAT) is applied. Furthermore, to consider the environmental concerns, the emission produced by thermal power plants is modeled using the emission cost function as a term of the objective function. The proposed model is tested on the standard cases of IEEE 10, and 38 units and the results are reported. The obtained results confirm that the proposed approach can effectively reduce the OOC. One of the main advantages of the proposed method is the reduction in computational burden due to the modeled uncertainties in solving the UUCP compared to the other proposed methods based on iterations while improving the final solutions. [ABSTRACT FROM AUTHOR]

• The proposed control method eliminates the reactive power sharing errors. • The proposed control method causes just small transient active power disturbances. • The main advantages are simplicity and non-dependence on MG configuration. In this paper, a control strategy has been presented to improve the reactive power sharing among distributed generation units in the islanded microgrids. The proposed control method is based on exerting a partial transient coupling between active and reactive powers. This partial coupling is performed by adding a small reactive power droop term in the conventional frequency droop controller during the compensation time. This coupling term causes a small active power disturbance that used to generate a compensation voltage signal. The suggested compensation scheme is enabled by the synchronized flag signal through a low-bandwidth communication link. To assess the performance and effectiveness of the suggested control approach, a MG with three DGs is simulated. The simulation results demonstrate that the reactive power is shared among DGs accurately. Simplicity, no need for additional communication links, non-dependence on MG configuration and insignificant transient disturbances are the main advantages of the proposed method. [ABSTRACT FROM AUTHOR]